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Toolbox for Biology-Informed data-assimilation to solve the inverse problem solution of pheromone propagation, i.e. to identify sources of pheromone emission, in particular insect pests. A specific focus is put on adding prior biological knowledge on insect behavior in the inference process.
Toolbox for Biology-Informed data-assimilation to solve the inverse problem solution of pheromone propagation, i.e. to identify sources of pheromone emission, in particular insect pests. A specific focus is put on adding prior biological knowledge on insect behavior in the inference process.
The target audiance is academic researcher interested in epidemiosurveillance for crops.
The target audience is academic researcher interested in epidemiosurveillance for crops.
* Free software: GNU Lesser General Public License v3 or later (LGPLv3+)
* Free software: GNU Lesser General Public License v3 or later (LGPLv3+)
@@ -35,7 +35,9 @@ Insect pests are a major threat for agricultural systems [@oerkeCropLossesPests2
...
@@ -35,7 +35,9 @@ Insect pests are a major threat for agricultural systems [@oerkeCropLossesPests2
# Statement of need
# Statement of need
Unlike other generic tools for data assimilation, such as DAPPER [@raanesDAPPERDataAssimilation2024], OpenDA [@ridlerDataAssimilationFramework2014] or [@nergerPDAFPARALLELDATA2005], `Pherosensor-toolbox` is a context-specific application-oriented package specifically designed to solve the inverse problem of inferring the source term (i.e. pheromone emitters position and emission rates) within a Chemical-Transport Model (CTM) modelling pheromone propagation in an agricultural landscape. Additional feature is the possibility to inform the data assimilation with insect behavior, such as the population dynamics partial differential equations (PDE), to get Biology-Informed Data-Assimilation (BI-DA). BI-DA aims to counter-balance data scarcity with prior biological knowledge. An other additional feature to come is optimal sensor placement tools to find the most informative sensor placement for assimilating the sensors data and inferring the source term of the CTM.
In the field of data assimilation for PDE and dynamic systems, existing packages provide tools to easily interface a dynamic system and an observation model and a collection of data assimilation algorithms, such as DAPPER [@raanesDAPPERDataAssimilation2024], OpenDA [@ridlerDataAssimilationFramework2014] or PDAF [@nergerPDAFPARALLELDATA2005].
Unlike these generic packages, `Pherosensor-toolbox` is a context-specific application-oriented package specifically designed to solve the inverse problem of inferring the source term (i.e. pheromone emitters position and emission rates) within a Chemical-Transport Model (CTM) modelling pheromone propagation in an agricultural landscape. Additional feature is the possibility to inform the data assimilation with insect behavior, such as the population dynamics partial differential equations (PDE), to get Biology-Informed Data-Assimilation (BI-DA). BI-DA aims to counter-balance data scarcity with prior biological knowledge. An other additional feature to come is optimal sensor placement tools to find the most informative sensor placement for assimilating the sensors data and inferring the source term of the CTM. The target audience is then academic researcher interested in epidemiosurveillance for crops.
